Hybrid adaptive procedure for estimation of psychometric functions

Abstract

Adaptive psychophysical procedures that have been described in the literature generally fall into one of two categories. (1) Simple procedures, such as UDTR and PEST, can be implemented without an on-line computer. The decision to change testing level is based on the outcome of the few most recent trials, and the final estimate of threshold is given by the final testing level or the average of a few testing levels. (2) "Maximum likelihood" methods require an on-line computer. A parametric form of the psychometric function is assumed, and after each trial a maximum-likelihood estimate of the parameters of the psychometric function is made on the basis of all preceding trials. This estimate is used to set the next testing level and to estimate threshold. We describe here a hybrid procedure, in which testing levels are determined by PEST and the final estimate of threshold is made by fitting an assumed psychometric function to all preceding trials. The PEST rules were tuned to yield results that were accurate and insensitive to errors in initial estimates of the psychometric function. These parameters differ from those that yield optimum results with classical PEST. Results of computer simulations and of experiments with human subjects are presented.